Plastic Workability and Rheological Stress Model Based on an Artificial Neural Network of SiCp/Al-7.75Fe-1.04V-1.95Si Composites

Abstract

SiCp/Al-Fe-V-Si composites exhibit complex deformation behaviors at both room and high temperatures because of the presence of SiC reinforcement particles and numerous fine dispersed Al12(Fe, V)3Si heat-resistant phases. In this work, an artificial neural network (ANN) constitutive model was established to study the deformation behavior of SiCp/Al-7.75Fe-1.04V-1.95Si composites over a wide temperature range based on uniaxial compression. Then, microstructural observation, finite element analysis, and processing maps were utilized to investigate the plastic workability. The results showed that the ANN model fit the experimental stress–strain curves with high accuracy, achieving an R2 value of 0.999. The ANN model was embedded into finite element software to study plastic deformation behaviors, which indicated that this model could accurately compute the plastic and mechanical response during the compressing process. Finally, a thermomechanical processing diagram was developed, revealing that the optimal processing parameters of the SiCp/Al-7.75Fe-1.04V-1.95Si composites were a deformation temperature of 450–500 °C and a deformation rate of 0.1–0.2 s−1.